Contrary to assumptions about user stickiness, consumers of AI models will quickly switch to a better-performing or cheaper alternative. The 22% drop in ChatGPT usage after new Gemini models were released demonstrates that brand loyalty is low when model performance is the key value proposition.

Counterintuitively, consumer AI apps like ChatGPT show more durable user loyalty than B2B developer tools. Developers can easily swap models via API calls, but consumers build habits and workflows that are harder to change, creating a more stable user base.

Unlike traditional enterprise software, the AI vendor landscape is exceptionally fluid. Ramp's data reveals monthly leadership shifts, such as Anthropic surpassing OpenAI in business usage and Cursor overtaking GitHub Copilot, indicating low switching costs and rapid innovation cycles.

A key trend TinySeed observes among AI-focused applicants is extremely high churn, often 10-20% per month. Even with rapid top-line growth, this level is deemed "catastrophic," indicating many new AI products struggle with defensibility and long-term customer value, making them risky investments despite the hype.

Despite significant history and memory built up in platforms like ChatGPT, power users quickly abandon them for models like Claude or Manus that provide superior results. This indicates that output quality is the primary driver of adoption, and existing "memory" is not a strong enough moat to retain users.

AI companies like OpenAI have shifted to monthly, incremental model updates. This frequent but less impactful release cadence means developers no longer feel strong loyalty to any specific model and simply switch to the newest version available, treating major AI models like commodities.

The generative video space is evolving so rapidly that a model ranked in the top five has a half-life of just 30 days. This extreme churn makes it impractical for developers to bet on a single model, driving them towards aggregator platforms that offer access to a constantly updated portfolio.

The AI landscape is uniquely challenging due to the rapid depreciation of both models (new ones top leaderboards weekly) and hardware (Nvidia launched three new SKUs in one year). This creates a constant, complex management burden, justifying the need for platforms that abstract away these choices.

A profound challenge in AI is that we lack the time to fully evaluate a model's intelligence on long-running tasks. Before we can discover a model's true capabilities, a new, more powerful generation is released, making the previous one obsolete and its full potential unknown.